Database Commons

a catalog of worldwide biological databases

Manteia is an integrative database available online at http://manteia.igbmc.fr which provides a large array of OMICs data related to the development of the mouse, chicken, zebrafish and human. The system is designed to use different types of data together in order to perform advanced datamining, test hypotheses or provide candidate genes involved in biological processes or responsible for human diseases. In this new version of the database, Manteia has been enhanced with new expression data originating from microarray and next generation sequencing experiments. In addition, the system includes new statistics tools to analyze lists of genes in order to compare their functions and highlight their specific features. One of the main novelties of this release is the integration of a machine learning tool called Lookalike that we have developed to analyze the different datasets present in the system in order to identify new disease genes. This tool identifies the key features of known disease genes to provide and rank new candidates with similar properties from the genome. It is also designed to highlight and take into account the specificities of a disease in order to increase the accuracy of its predictions. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

The function of genes is often evolutionarily conserved, and comparing the annotation of ortholog genes in different model organisms has proved to be a powerful predictive tool to identify the function of human genes. Here, we describe Manteia, a resource available online at http://manteia.igbmc.fr. Manteia allows the comparison of embryological, expression, molecular and etiological data from human, mouse, chicken and zebrafish simultaneously to identify new functional and structural correlations and gene-disease associations. Manteia is particularly useful for the analysis of gene lists produced by high-throughput techniques such as microarrays or proteomics. Data can be easily analyzed statistically to characterize the function of groups of genes and to correlate the different aspects of their annotation. Sophisticated querying tools provide unlimited ways to merge the information contained in Manteia along with the possibility of introducing custom user-designed biological questions into the system. This allows for example to connect all the animal experimental results and annotations to the human genome, and take advantage of data not available for human to look for candidate genes responsible for genetic disorders. Here, we demonstrate the predictive and analytical power of the system to predict candidate genes responsible for human genetic diseases.